Graph-Symbolic Policy Enforcement and Control (G-SPEC)

• Graph-Symbolic Policy Enforcement and Control (G-SPEC) is a neuro-symbolic framework that combines probabilistic LLM planning with deterministic graph-based verification to enhance safety in telecom networks.
• It leverages a tripartite Governance Triad—including a Network Knowledge Graph, a telecom-specific LLM, and SHACL-based governance—to mitigate issues like topology hallucinations and policy non-compliance.
• Experimental evaluations on an Open5GS 5G core topology demonstrate significant improvements, achieving 94.1% remediation success and zero safety violations compared to GPT-4 baselines.

Graph-Symbolic Policy Enforcement and Control (G-SPEC) is a neuro-symbolic framework designed to enable safe, intent-driven agentic AI in 5G Standalone and prospective 6G autonomous network environments. G-SPEC addresses the operational limitations of static automation and deep reinforcement learning by combining probabilistic LLM planning with deterministic, graph-based verification. The architecture systematically mitigates stochastic risks, such as topology hallucinations and policy non-compliance, extending feasibility for Service Management and Orchestration (SMO) layers in telecom networks (Vijay et al., 23 Dec 2025).

1. Architectural Principles: The Governance Triad

G-SPEC is built on a tripartite “Governance Triad” comprising: (i) a Network Knowledge Graph (NKG), (ii) a Telecom-Specific Large Action Model (TSLAM-4B), and (iii) a SHACL-based Governance Plane.

• Layer 1: Network Knowledge Graph (NKG)
  • Formalized as $G_t = (V, E)$, where $V$ is the set of network functions and $E$ encodes relations $$(v_i, v_j, \text{timestamp}, \text{interface-type})$$.
  • Implements the 3GPP TS 28.623 ontology within Neo4j, providing an authoritative ledger of topology and runtime attributes.
• Layer 2: TSLAM-4B Probabilistic Planner
  • A 4B-parameter LLM, quantized to 4-bit, pre-trained on telecom-specific logs achieving 93% domain accuracy.
  • Produces a Chain-of-Thought (CoT) trace: Observation → Diagnosis → Plan, with policy output $\pi(a|S_t, I)$ that is inherently stochastic: $P(a\notin A_{\text{valid}}|S_t) > 0$.
• Layer 3: SHACL-Based Governance Plane
  • Enforces 88 SHACL shapes covering topological, resource, state, temporal (“freshness”), and blast-radius constraints.
  • Validates all hypothetical graph mutations prior to enactment, ensuring atomic rejection if any violation or extraneous targets are detected.

The end-to-end workflow progresses from operator intent submission, subgraph extraction, LLM planning, simulated verification, atomic plan acceptance/rejection, orchestrator execution, and full audit trace logging.

2. Neuro-Symbolic Planning and Policy Verification

G-SPEC integrates probabilistic reasoning with formal deterministic control.

• Probabilistic Planning
  • TSLAM-4B generates agent policies $\pi(a|S_t, I)$ via next-token prediction, subject to stochastic error $$P_{\text{err}} = P(a\notin A_{\text{valid}}|S_t) > 0$$.
• Deterministic Verification
  • The graph model $G_t = (V, E)$ requires any proposed subgraph mutation $g \subseteq G_t$ to satisfy $\Phi(a, G_t)$ per SHACL policy set $P$, expressed as tuples $(\text{Target}, \text{Constraint}, \text{Action})$.
  • The verification function:

  $$\text{Verify}(a, G) = \begin{cases} \text{TRUE} & \text{if } G'\vDash P \land \text{targets}(a) \subseteq V \ \text{FALSE} & \text{otherwise} \end{cases}$$ - Additional guards include blast-radius limits ($\delta(S_t, S_{t+1})$ capped at $\pm20\%$ capacity change) and freshness constraints (timestamp $\tau$ must satisfy $\text{now}-\tau < \Delta_\text{safe}$ with default 15 s).

3. Experimental Evaluation and Comparative Metrics

G-SPEC’s efficacy was assessed on a Kubernetes-deployed Open5GS 5G core topology (450 nodes, 1,200 edges) using Neo4j NKG, TSLAM-4B (92.3% validation acc.), and 500 synthetic fault scenarios. The following key metrics were measured and compared to GPT-4 zero-shot (ZS) and fine-tuned (FT) baselines:

Metric	GPT-4 (ZS)	GPT-4 (FT)	G-SPEC
Remediation Success	82.4%	86.8%	94.1%
Safety Violations	8.2%	2.1%	0.0%
Hallucination Rate	14.6%	8.2%	0.2%
Inference Latency	2.1 s	2.1 s	2.24 s
Validation Overhead	—	—	142 ms

Statistical tests (Mann-Whitney U, $p < 0.001$) confirm significant improvements in remediation, safety, and hallucination rates.

4. Component Analysis and Scalability

Ablation analysis isolates contributions from NKG, SHACL, and TSLAM-4B components:

Configuration	Remediation	Hallucinations	Safety Violations	Overhead
Full G-SPEC	94.1%	0.2%	0.0%	142 ms
NKG Validation	76.2%	8.4%	7.8%	12 ms
SHACL Policies	89.3%	1.2%	2.3%	18 ms
TSLAM	86.8%	8.2%	2.1%	138 ms

Efficacy: NKG validation drives 68% of gains, SHACL policies 24%, and TSLAM-4B only 8%.

Scalability is characterized empirically:

Nodes	Edges	Subgraph $k$	Valid. Latency (ms)	Multiplier
450	1.2 K	12	142	1.00×
10 K	30 K	31	196	1.38×
100 K	300 K	48	314	2.21×

Validation latency fits $L(k) \approx 142 \cdot (k/12)^{1.2}$, indicating sublinear scaling law $O(k^{1.2})$ suitable for SMO-layer contexts.

5. Domain Integration and Operational Constraints

G-SPEC is optimized for the SMO layer, particularly non-real-time RIC/SMO windows (5–60 s loop times), where its 142 ms overhead constitutes less than 1% relative to typical pod spin-up latencies (15–45 s). The system is not suitable for Near-RT (<10 ms) or real-time MAC scheduling (<1 ms) due to verification latency.

• Reliance on NKG freshness introduces risk of false positives and negatives; latency or staleness windows diminish reliability.
• SHACL corpus completeness is assumed—unmodeled failure modes remain a risk vector.
• Experimental results derive exclusively from synthetic scenarios and Open5GS deployments, absent multi-vendor complexity.

6. Research Significance, Limitations, and Future Work

Empirical results demonstrate the necessity of neuro-symbolic hybridization in agentic AI orchestration: deterministic graph validation is imperative to eliminate LLM-driven stochastic hazards such as hallucinations or policy breaches. Ontological grounding in NKG delivers the majority safety gain (68%) compared to pure LLM tuning (8%).

G-SPEC's validation latency and RAM usage (~250 MB at 100K nodes) render it deployable in SMO-layer environments. Notable limitations include reliance on synthetic data, single-vendor simulations, and static SHACL policies. Trust in operator intent and NKG ingestion lag are potential adverse factors.

Future research directions are identified:

1. Tier-1 carrier trials on real 5G networks with 100K+ nodes.
2. Active topology reconciliation via on-demand telemetry probes.
3. Ontology alignment across ONAP/ONF and multi-vendor scenarios.
4. Robustness against adversarial operator intents.
5. Extension for 6G (quantum-safe routing, native AI inference).
6. Automated synthesis of SHACL constraints from formal specifications.

A plausible implication is that G-SPEC establishes a reference architecture for safe, verifiable agentic AI in emerging autonomous mobile networks, combining symbolic verification with probabilistic action planning (Vijay et al., 23 Dec 2025).